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We investigate the power and performance improvement in image acquisition devices by the use of CAS (Compressed Acquisition Scheme) and DNN (Deep Neural Networks). Towards this end, we propose a novel image acquisition scheme HCAS (Hardware based Compressed Acquisition Scheme) using hardware-based binning (downsampling), bit truncation and JPEG compression and develop a deep learning based reconstruction network for images acquired using the same. HCAS is motivated by the fact that in-situ compression of raw data using binning and bit truncation results in reduction in data traffic and power in the entire downstream image processing pipeline and additional compression of processed data using JPEG will help in storage/transmission of images. The combination of in-situ compression with JPEG leads to high compression ratios, significant power savings with further advantages of image acquisition simplification. Bearing these concerns in mind, we propose DRCAS (Deep Restoration network for hardware based Compressed Acquisition Scheme), which to our best knowledge, is the first work proposed in the literature for restoration of images acquired using acquisition scheme like HCAS. When compared with the CAS methods (bicubic downsampling) used in super resolution tasks in literature, HCAS proposed in this paper performs superior in terms of both compression ratio and being hardware friendly. The restoration network DRCAS also perform superior than state-of-the-art super resolution networks while being much smaller. Thus HCAS and DRCAS technique will enable us to design much simpler and power efficient image acquisition pipelines.

In this work, the behaviour of bit error rates for both conventional and massive MIMO systems with high order constellations, which are essential to achieve spectral efficiency for 5G standard communications, has been evaluated. We have used real-domain Schnorr Euchner enumeration with K-best algorithm to reduce computational complexity of detection. The results, presented in this letter, have outperformed existing detection algorithms in terms of complexity and BER, especially in low SNR regions, for both massive and conventional MIMO systems. We performed simulations for N $\times$ N MIMO system, where N = 8, 25, 40, 50, 60, 80, 100 and 120, for both 256-QAM and 1024-QAM high order transmission systems as per the latest 3GPP standards for 5G systems and beyond. All the analyses and results that are given in this letter are from our MIMO detector, prior to usage of error correction decoder.

Advances in CMOS technology have made high resolution image sensors possible. These image sensor pose significant challenges in terms of the amount of raw data generated, energy efficiency and frame rate. This paper presents a new design methodology for an imaging system and a simplified novel image sensor pixel design to be used in such system so that Compressed Sensing (CS) technique can be implemented easily at the sensor level. This results in significant energy savings as it not only cuts the raw data rate but also reduces transistor count per pixel, decreases pixel size, increases fill factor, simplifies ADC, JPEG encoder and JPEG decoder design and decreases wiring as well as address decoder size by half. Thus CS has the potential to increase the resolution of image sensors for a given technology and die size while significantly decreasing the power consumption and design complexity. We show that it has potential to reduce power consumption by about 23%-65%.